{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T01:07:01Z","timestamp":1789175221441,"version":"build-2803163510"},"reference-count":39,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270607"],"award-info":[{"award-number":["32270607"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002852","name":"State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center","doi-asserted-by":"publisher","award":["NO. SKL2024K03"],"award-info":[{"award-number":["NO. SKL2024K03"]}],"id":[{"id":"10.13039\/501100002852","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background &amp; Aims<\/jats:title>\n                    <jats:p>As central determinants of viral control and immunopathology, hepatitis B virus (HBV)\u2010specific T\u2010cell responses provide more clinically meaningful information than routine biochemical and virological markers. Nevertheless, most existing prediction models rely primarily on clinical parameters and rarely incorporate HBV\u2010specific T\u2010cell responses, potentially limiting their predictive performance and biological interpretability. To develop an accurate and interpretable model for predicting hepatitis progression, we evaluated diverse machine learning (ML) methods integrating multisource data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We enrolled a cohort of 479 patients and divided them into training and testing cohorts based on admission time. Clinical data, treatment regimens, and HBV\u2010specific T\u2010cell immune responses were collected. A comprehensive benchmarking of 10 ML models was conducted through 5\u2010fold cross\u2010validation on the training cohort across varying feature combinations and algorithmic performances. The ML models were independently evaluated on the testing cohort, and significant predictive factors were identified.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Based on alanine aminotransferase (ALT) levels at 6\u2010 and 12\u2010month follow\u2010ups, the patients were stratified into hepatitis (ALT\u2009&gt;\u200940\u2009U\/L) and non\u2010hepatitis (ALT\u2009\u2264\u200940\u2009U\/L) groups. The predictive performance was significantly improved after integrating clinical indicator features (CIF) and HBV\u2010specific T\u2010cell features (STCF). An XGBoost model based on six selected features (three CIF and three STCF) demonstrated especially robust performance, achieving AUCs of 0.874 (validation) and 0.880 (testing) at 6\u2009months follow up and 0.851 (validation) and 0.845 (testing) at 12\u2009months. To facilitate clinical application, a web\u2010based tool was developed for personalized risk assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Incorporating HBV\u2010specific T\u2010cell responses into the predictive framework improves the prediction of disease progression among HBV\u2010infected individuals. The developed interpretable model is a potentially valuable tool for early risk stratification, facilitating proactive monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.70758","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T04:46:41Z","timestamp":1782535601000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>HBV<\/scp>\n                    \u2010Specific T Cell Responses for Short\u2010Term Hepatitis Progression Prediction: A Comparative, Interpretable Machine Learning Study"],"prefix":"10.1111","volume":"46","author":[{"given":"Sidu","family":"Feng","sequence":"first","affiliation":[{"name":"Key Laboratory of DGHD, MOE, School of Life Science and Technology Southeast University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yandan","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of 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